How do you evaluate an AI underwriting platform for a P&C carrier or MGA?

A buyer should evaluate AI underwriting platforms on five axes: (1) audit trail completeness — every LLM call logged with prompt + model + trace; (2) data fabric breadth — does it integrate FEMA, ISO, OFAC, treaty data, or just call an LLM; (3) multi-agent architecture vs single chatbot; (4) deployment model — bring-your-own-LLM, VPC, on-premise availability; (5) configurability — appetite rules, decision authorities, statutory letters per state. Anything that fails axis 1 will fail the first regulator exam.

Evaluating an AI underwriting platform requires asking five questions in order. If a vendor fails the first question, the rest don't matter.

1. Audit trail — is every LLM call logged?

  • Every prompt, every model name, every output, every trace id, every human decision, every downstream notification must log to an append-only table.
  • The artifact must be exportable as a single document for a state DOI data request, a NAIC market-conduct exam, or a Lloyd's coverholder quarterly review.
  • Ask for a sample audit pack from a recent decision. If the vendor can't produce one in the demo, walk.

2. Data fabric — what's the platform grounded on?

  • A platform that only calls an LLM is a chat wrapper, not an insurance tool.
  • The minimum data fabric for US commercial property: FEMA NFIP / NFHL, NOAA HURDAT2, OFAC SDN, ZIP / Census, OS Postcodes (for UK).
  • Add ISO ClaimSearch + NICB Forewarn for claims.
  • For specialty: Verisk, D&B, SEC EDGAR.
  • Test: ask for a postcode and have the platform return the FEMA flood zone with a citation. If it can't, the fabric is missing.

3. Architecture — multi-agent or single chatbot?

  • Single-agent platforms collapse at the third specialist task. You'll hit the context window or the prompt complexity ceiling.
  • Multi-agent platforms route each task (parse, risk, flood, pricing, compliance, treaty, portfolio, memo) to a dedicated model bracket. Each agent is replaceable.
  • Bonus: ask if agents run in parallel where independent. Sequential chains are 5–10× slower without an architectural reason.

4. Deployment — where does our data go?

  • Multi-tenant cloud. Easiest to deploy, hardest to procure.
  • VPC / dedicated tenant. Required for most large carriers.
  • On-premise. Required for some Lloyd's syndicates and government-adjacent carriers.
  • Bring-your-own-LLM. Your data routes through your Bedrock / Vertex / Anthropic-direct account. The vendor is the orchestration layer; the model perimeter stays inside your VPC.

If the vendor's only deployment is multi-tenant cloud on their LLM keys, procurement will not approve at any enterprise carrier.

5. Configurability — does it match our filed product?

  • Appetite filters per state, per line, per occupancy. Editable without a code release.
  • Decision authorities: who can bind up to what TSI / premium / loss reserve. Audit-logged when over-authority pays trigger refer-to-senior.
  • Statutory letters per state with cited regulation. UCSPA-compliant for the four largest states minimum (CA, FL, NY, TX).
  • STP rules — auto-bind, auto-decline, refer thresholds — owned and edited by the carrier's CUO.

A platform that meets all five gets you to a 90-day pilot. The 91st-day question — is it actually changing your loss ratio — answers itself by month three.

Updated 2026-05-17·underwritingcompliance
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Vortic is the audit-grade multi-agent platform for P&C carriers and MGAs — submission to bound risk in ~30 seconds with a regulator-ready audit trail.

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